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Stream/Area:
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Subject:
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Paper ID:
10.55041/IJSREM48815
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Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
This study investigates the integration of Artificial Intelligence (AI) as a co-pilot in aviation. As aviation increasingly embraces automation, concerns about safety, pilot roles, and ethical implications grow. The research emphasizes the necessity for human-AI collaboration in which AI enhances pilot capabilities without replacing them. Using a mixed-methods approach that includes literature review, case analysis, and stakeholder input, the study provides a model for safely integrating AI into flight operations. Key findings support that AI should assist rather than replace pilots, maintaining human oversight in high-stakes environments. The thesis concludes with policy and design recommendations for AI integration in aviation. This comprehensive study explores the transformative potential of Artificial Intelligence (AI) in modern commercial aviation, specifically emphasizing the concept of AI as a co-pilot. With rising operational complexity, global pilot shortages, and increasing demand for enhanced safety and efficiency, AI presents itself as a promising technological innovation. It has the capacity to provide real-time flight data interpretation, system anomaly alerts, autonomous recommendations, and task automation that collectively enhance situational awareness and reduce pilot workload. However, the expansion of AI usage in aviation also introduces critical concerns around pilot-AI interaction, explainability of AI systems, cybersecurity risks, and ethical responsibility in cases of failure. The research argues for a balanced integration model where AI supports—rather than supplants—human pilots. The idea of a “co-pilot AI” is conceptualized to highlight systems that work in tandem with human decision-makers, offering real-time insights while respecting human authority. Human-in-the-loop (HITL) design frameworks, explainable AI (XAI) principles, and trust-calibration models were central to the theoretical foundation of this research. A mixed-methods
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Publication Date
5/28/2025
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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The authors declare no conflict of interest related to this research work.
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